Hi Aga, comments below
Burzynska, Aga wrote:
Dear all, I just wanted to double-check if I am doing the right thing.
I am transferring fMRI data from FSL to the surface in Freesurfer to test whether cortical thickness is related to BOLD signal change.
I used the steps described here: http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/FslFeatFreeSurfer
I did the following:
- I carried out Freesurfer reconstruction for all subjects
- I performed 1st level analysis for fMRI data in FSL, separately for
each run, without smoothing 3) Then I obtained good registration of FEAT example_func of each run to the subject's anatomical using FLIRT. This I have done already for a specific contrast of interest.
Did you not use reg-feat2anat for this step? Did you register the z-map itself? If so, that is not the right thing to do, it should be the example_func.
The above steps have been performed with Freesurfer version earlier than 5.0. Will there be a problme if I continue the steps below with the newest stable version?
This will not be a problem.
Now I plan: 4) Combine the 3 runs/subject on the surface by averaging them with smoothing. As this is within-subject, my guess is that 5mm will be the best.
Sounds good. Make sure to use the copes and not the zstats
- Concatenate the images for all subjects to perform analyses
I am not sure about the following points:
- what are the units of the zstat images transferred onto the surface?
Is it still the logarithmic scale as for thickness analyses?
I'm not sure what you mean. As I mention above, you should use the copes and not the zstats for the next higher level analysis.
- I will want to create a fMRI-based ROI and then investigate the
thickness especially within this region. i know I can extract mean ROI values and use other statistical software, but how can I perform vertex-wise analysis within this ROI? I assume qdec does not allow this, so only the DODDS analysis is possible.
Which stat map do you want to derive the ROI from? The group map?
doug
Thank you a lot for your support! Best, Aga
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